International Journal of Climatology最新文献

Under global warming, heatwaves (HWs) are exerting increasing impacts on human life. In order to explore the thermal sensation of HWs under the combined influence of air temperature, humidity, radiation and wind speed, this study utilized the hourly Universal Thermal Climate Index (UTCI) data from the ECMWF Reanalysis v5 (ERA5) to define daytime, night-time and compound HWs, and then comparatively analysed their occurrences in China during 1981–2020. Results revealed that daytime HWs affect the majority of regions in China with the exception of the Tibetan Plateau, while both night-time and compound HWs mainly strike the Yangtze River Basin and the Guangdong–Guangxi region in southeastern China. Additionally, it is noteworthy that a substantial portion of regions experiencing night-time HWs also encounter compound HWs. The frequency and intensity of all three types of HWs show increasing trends generally in regions where HWs occur. The spatial coverage of HWs grows at rates of 8.05%/10a, 2.11%/10a and 1.62%/10a for the daytime, night-time and compound types, respectively. For each summer month, the spatial coverage of daytime HWs notably surpasses that of night-time and compound HWs. Further classifying HWs with intensity, it is found that the spatial coverage of both moderate and severe HW events exhibits discernible rising trends across all three types of HWs. Furthermore, regions with higher HW frequencies are more likely to experience severe HWs as well as record-breaking ones. Additionally, the spatial coverage of daytime, night-time and compound record-breaking HWs is expanding.

Snow is particularly impacted by climate change and therefore there is an urgent need to understand the temporal and spatial variability of depth of snowfall (HN) trends. However, the analysis of historical HN observations on large-scale areas is often impeded by lack of continuous long-term time series availability. This study investigates HN trends using observed time series spanning the period 1920–2020 from 46 sites in the Alps at different elevations. To discern patterns and variations in HN over the years, our analysis focuses also on key parameters such as precipitation (P), mean air temperature (TMEAN), and large-scale synoptic descriptors, that is, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Atlantic Multidecadal Oscillation (AMO) indices. Our findings reveal that in the last 100 years and below 2000 m a.s.l., despite a slight increase in winter precipitation, there was a decrease in HN over the Alps, especially for southern and low-elevation sites. The South-West and South-East regions experienced an average loss of 4.9 and 3.8%/decade, respectively. A smaller relative loss was found in the Northern region (2.3%/decade). The negative HN trends can be mainly explained by an increase of TMEAN by 0.15°C/decade. Most of the decrease in HN occurred mainly between 1980 and 2020, as a result of a more pronounced increase in TMEAN. This is also confirmed by the change of the running correlation between HN and TMEAN, NAO, AO over time, which until 1980 were not correlated at all, while the correlation increased in later years. This suggests that in more recent years favourable combinations of temperature, precipitation, and atmospheric pattern have become more crucial for snowfall to occur. On the other hand, no correlation was found with the AMO index.

Climate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CMCC-ESM2 and MRI-ESM2-0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias-correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2-4.5, SSP3-7.0 and SSP5-8.5). First, bias-correction was performed on ERA5-Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5-Land reanalysis data was considered as measured data. Based on the corrected ERA5-Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias-corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5-Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM-RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC-ESM2 revealed the highest RMSE in both SSP2-4.5 and SSP3-7.0 amounting to 331.74 and 201.84 mm·year⁻¹, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5-8.5, notably in the case of ACCESS-ESM1-5 (RMSE = 431.39 mm·year⁻¹), while the RMSE reduced after using QM-RF method (197.75 mm·year⁻¹). Obviously, a significant enhancement in results was observed upon implementing the QM-RF combination method in CMCC-ESM2 under both SSP2-4.5 (RMSE = 139.30 mm·year⁻¹) and SSP3-7.0 (RMSE = 151.43 mm·year⁻¹) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year⁻¹, respectively. Although each bias-corrected model output was evaluated individually, multi-model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM-RF method revealed the lower errors in correcting model outputs, we used the QM-RF technique to create the MME. Based on SSP2-4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3-7.0. Moreover, MME projected dramatic declines under SSP5-8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2-4.5, SSP3-7.0 and SSP5-8.5.

This paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend-like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.

The current study investigates the modulation of the tropical cyclone (TC) frequency (TCF) over the Bay of Bengal (BoB) by the southern annular mode (SAM). The analysis reveals that the SAM–TCF relationship during October–November–December has undergone interdecadal changes from significant during 1971–1994 to insignificant during 1995–2021. This contrasting influence of the SAM on the TCF occurrence is also echoed in the large-scale environmental variables conducive to forming tropical cyclones (TCs). Based on the possible mechanism, we found that the SAM can imprint tripole sea surface temperature (SST) patterns in the southern Indian Ocean via altering surface wind speed from 1971 to 1994. The SAM-related tripole SST pattern induces the surface-level anticyclone anomaly, which enhances the south easterlies towards the western equatorial Indian Ocean. Such intensified anomalous wind crosses the equator and diverts towards the east to form the cyclone anomaly in the BoB. Meanwhile, at 200 hPa, the anomalous anticyclone over western Australia induces divergent wind flows over the study region. Consequently, the ascending motion in BoB promotes the tropical cyclone generation. During 1995–2021, however, the SAM is associated with the dipole SST pattern in the southern Indian Ocean. Correspondingly, the SAM-related dipole SST yields anomalous atmospheric circulations confined to the Southern Hemisphere and eventually fails to impact the formation of TCs in the northern Indian Ocean, where the study region is located. The findings of this research can be useful in advancing our knowledge of the interannual variability of TCs activity in the BoB based on the remote climate signal.

Climate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record-breaking 43-day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience-building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA.

Droughts, depending on their nature, have had devastating consequences, including crop destruction, famine and millions of deaths, particularly in countries like India that heavily rely on rainfall for agriculture. The present study aims to quantify the linkage between meteorological, agricultural and hydrological drought at a high spatial resolution across India. These connections were established by developing various drought propagation metrics followed by subsequent correlation analysis, lag analysis and clustering. Standard Precipitation Index (SPI), Deviation in NDVI (Dev-NDVI) and GRACE Drought Severity Index (GRACE-DSI) were used to represent meteorological, agricultural and hydrological droughts. Run theory with thresholds of −1, −0.5 and −0.05 were used to delineate the drought events for meteorological, hydrological and agricultural droughts, respectively. Furthermore, multivariate K-means clustering based on factors such as drought duration, latitude, longitude, severity, propagation and recovery speeds was done to create spatial clusters having similar drought characteristics. Correlation analysis showed the highest average correlations at a lag of around 7–8 months between meteorological and hydrological drought, a lag of 1–2 months in case of meteorological and agricultural drought and a lag of 3–4 months between agricultural and hydrological drought. The analysis of drought duration indicated that, on average, meteorological drought in India lasted for 2.34 months, while agricultural drought lasted for 3 months, reflecting a 26.5% increase, whereas hydrological drought lasted for 5.22 months, indicating a notable 123% increase. This increase in average drought duration as it propagates from meteorological to agricultural to hydrological drought can be attributed to the lengthening property of drought propagation. Clustering analysis reveals presence of five homogeneous drought clusters. Additionally, cluster analysis reveals that for meteorological and agricultural droughts arid regions showed the highest severity whereas for hydrological droughts north Indian states including Punjab and Haryana showed the highest severity.

This decade-long study from the INTERCONF programme addresses the data gap on ocean dynamics in the Southern Hemisphere. Focusing on the Brazil-Malvinas Confluence (BMC), we investigated the effect of temperature differences between the warm Brazil Current (BC) and the cold Malvinas Current (MC) on water vapour in the marine atmospheric boundary layer (MABL). Our results show a clear distinction: warmer BMC waters have 32% more water vapour (2 kg·m⁻² on average) compared to the MC. This highlights the direct link between ocean temperatures and atmospheric processes. Analysis of radiosonde data alongside satellite measurements showed better agreement over cooler waters with lower water vapour, leading to lower variability. This suggests less atmospheric turbulence and improved data compatibility, especially for satellite retrievals such as AIRS. Comparisons between reanalysis data (CFSR), satellite sounders (AIRS) and radiosondes (RS) showed consistent air temperature profiles, with average errors within the 10% threshold for satellite measurements. While capturing humidity variations remains a challenge, especially at high concentrations (indicated by higher mean squared error values), our study highlights the reliability of satellite data, particularly over the cold BMC region. This research highlights the importance of studying the interactions between ocean fronts and atmospheric phenomena for a complete picture of Southern Hemisphere ocean dynamics. It offers valuable insights for scientists across disciplines, providing a broad perspective on the results and their significance in different contexts.

This study explored the interannual characteristics of the out-of-phase pattern of summer precipitation over northern China during the past years, as well as the possible underlying mechanisms. The out-of-phase pattern is characterized by the positive precipitation anomaly in Northwest China (NWC) and the negative anomaly in North China (NC). Our analyses indicate that the variation of Asian westerly jet (AWJ) is found evidently associated with this out-of-phase pattern of summer precipitation. The meridional displacement of AWJ induces opposite trends in water vapour transport and circulation anomalies between NWC and NC, leading to the out-of-phase pattern. Further analyses suggest that the anomalies of Arctic sea ice concentration (SIC), North Atlantic and Northwest Pacific sea surface temperature (SST) can impact the variation of AWJ by triggering the Eurasian (EU), polar-Eurasia (POL) and Pacific-Japan (PJ) teleconnection patterns, respectively, which in turn modulate the formation of this pattern. The precipitation in NWC is primarily affected by the North Atlantic and Northwest Pacific SST anomalies, whereas the precipitation in NC is notably influenced by both SST anomalies and the Arctic SIC anomaly. However, the role of SST anomalies on precipitation in NWC and NC exhibits an evident contrast, leading to the out-of-phase pattern of summer precipitation.

This study examines the asymmetry in the Indian summer monsoon rainfall (ISMR) response over India and its four homogeneous regions to two distinct types of temporal evolution in La Niña. We have shown this uneven response by analysing the large-scale dynamics over tropical Indo-Pacific region for the period 1951–2022. We have identified two types of La Niña events during monsoon season (June–September) based on whether they evolved from El Niño or La Niña from preceding boreal winter season (December–February). India receives significantly more (less) rainfall during La Niña years, when it was preceded by El Niño (La Niña) in the preceding winter. We further observed the spatial diversity of rainfall over India with a northeast–southwest dipole pattern. When La Niña years were preceded by El Niño, positive surface pressure anomaly over west-north Pacific, low-level westerlies and moisture transport favoured the rainfall over south peninsula and west-central India. Whereas moisture divergence associated with anomalous lower-tropospheric anticyclone over west-north Pacific suppressed the rainfall over Indo-Gangetic plains. However, when La Niña years were preceded by La Niña in winter, the absence of westerlies and weak moisture transport subdued the rainfall over south peninsula and west-central India. At the same time, moisture convergence and a greater number of monsoon depressions favoured rainfall over north-west India. This study also looked at how well eight Copernicus Climate Change Service (C3S) models predicted ISMR and SST for two types of La Niña with April initial conditions during the period 1993–2016. Models were able to capture the spatial pattern of SST anomalies over Indo-Pacific Ocean, but all models could not capture the spatial pattern of ISMR. However, in terms of intensity, six out of eight models could predict more (less) ISMR when it was preceded by El Niño (La Niña), coinciding with the observed anomaly.